How Can Programming Assignments Evolve for AI-Assisted Learning?

Programming assignments have always turned what students learn into something they can build. But the way students approach them is changing.

Michael Parker, a second-year university student, noticed it at a programming competition where AI tools weren’t allowed. Classmates who usually started every task by asking an AI assistant to plan and write the code struggled badly. His conclusion: “Despite what they believe, it turns out they had never learned to code; they had learned to ask an AI.” DEV Community

That leaves instructors with a new question: what should a programming assignment ask students to demonstrate now?

The answer is not necessarily to remove AI. A 2025 systematic review of 40 empirical studies found that using AI well in programming education depends on intentional teaching strategies and thoughtfully designed assessment. It also warned that overreliance could weaken students’ higher-order thinking.

A functioning program does not provide a complete picture.

A student can submit code that produces the expected output without understanding how it works. That has always been possible, but AI makes it far easier to reach a working solution without going through the problem-solving process.

That provides instructors a reason to look beyond the final output. An assignment could ask students to debug an existing program, handle an unfamiliar situation, explain a design decision, or modify a solution when the requirements change.

Correcting AI’s code is harder than it looks. A 2025 study of more than 200 undergraduates in Israel and Germany found that students scored significantly lower when asked to correct code generated by a large language model than on traditional instructor-designed tasks. The researchers concluded that programming education “must also focus on developing students’ skills in critically evaluating and correcting AI-generated content.”

The question becomes less about whether students can produce code and more about whether they understand what the code does.

The Learning Objective Should Come First

AI should not decide what a programming assignment is trying to teach.

If students are learning loops, the assignment should give them a problem in which loops matter. If the objective is debugging, debugging should be part of the work.

A better assignment starts with the learning outcome and then decides whether, and how, AI should be used.

AI Can Become Part of the Learning Process

Keeping AI out of every assignment may not reflect the environment students will meet after college. Instead, an assignment can make students responsible for evaluating what AI produces.

For example, students could use AI to generate a first solution, then test it against different inputs, find an error, improve the code, and explain the changes they made. That shifts the task from accepting generated code to questioning and validating it.

Generated code can be useful without being correct.

Variation Can Make Assignments More Meaningful

When every student receives the same predictable problem, it becomes easier to reuse an existing solution.

Meaningful variation changes that. Different data sets, constraints, scenarios, or edge cases ask students to apply the same concept in slightly different situations, so they have to engage with the problem they were actually given.

For instructors, though, good variations add work. Each version needs its own test cases, instructions, evaluation criteria, and starter material.

Where Lab.Computer Fits

AI is not only changing how students approach assignments. It can also reduce the repetitive work of creating them.

Lab.Computer’s AI Assignment Generator lets instructors enter a learning objective, topic, difficulty level, and programming language, then generates problem statements, public and hidden test cases, rubrics, starter code, and instructions. That gives instructors more room to create variations and more time to decide whether each task truly fits the course.

Lab.Computer also supports both automated and manual grading with visible and hidden test cases, so instructors can rely on automated checks while still reviewing and adjusting student work.

That balance matters: a student’s ability to explain a decision or improve a generated solution tells an instructor something a passing test case cannot.

Assignments Can Prepare Students for AI-Assisted Work

Students will increasingly work alongside AI. Writing code still matters, but so does evaluating code that someone, or something, else produced.

Instead of asking only, “Can you write this program?" an assignment can also ask:

“Can you understand it, test it, debug it, improve it, and explain why it works?”

That is a more meaningful challenge for students and a more useful signal for instructors. The goal is not to make programming assignments harder for AI. It is to make them more valuable for learning.

Frequently Asked Questions

Should students be allowed to use AI for programming assignments?

There is no single approach for every course. The decision should depend on the learning objective and the skills the assignment is meant to measure.

How can instructors design programming assignments for the AI era?

Start with the learning objective and create tasks that require students to apply concepts, test solutions, debug problems, handle unfamiliar situations, or explain their reasoning.

Can AI help instructors create programming assignments?

Yes. AI can help generate problem statements, test cases, rubrics, starter code, and instructions, while instructors review and adapt the material for their course.

How can instructors know whether students understand AI-generated code?

Assignments can include debugging, modification, testing, explanation, and evaluation tasks that require students to show their own understanding rather than simply submit generated code.